协议无关组播-稀疏模式是目前应用最广泛的组播路由协议之一。
Protocol-independent multicast sparse mode(PIM-SM) is one of the most widely used multicast routing protocols.
并分别对密集模式组播路由协议的扩散和剪枝机制、稀疏模式组播路由协议中的RPT树到SPT树的切换过程进行了详细的探讨。
In this thesis, we also discuss the mechanism of Flooding and Pruning in Dense-Mode multicast routing protocol and the process of switch from RPT to SPT in Sparse-Mode routing protocol.
人脸识别实质是稀疏超高维空间、典型的小样本模式识别问题。
Face recognition is essentially a typical small-sample pattern recognition problem in sparse hyper-high dimensional space.
取消绑定模式还适用于类似于电子表格的表或稀疏填充的表。
Unbound mode is also suited for spreadsheet-like or sparsely populated tables.
稀疏表征理论在模式识别中的应用引起广泛的关注。
Very recently, the sparse representation theory in pattern recognition arouses widespread concern.
通过聚类，人们能够识别密集的和稀疏的区域，因而发现全局的分布模式，以及数据属性之间有趣的相互关系。
By clustering, one can identity dense and sparse regions, therefore, discover overall distribution patterns and interesting correlations among data attributes.
该文方法保证跨尺度自相似集具有相同的稀疏性模式，能更有效地利用图像的自相似性先验信息，提高算法的自适应性。
This method keeps the patches pairs the same sparsity patterns, and makes efficiently use of the self-similar information. The adaptability is enhanced.
该文方法保证跨尺度自相似集具有相同的稀疏性模式，能更有效地利用图像的自相似性先验信息，提高算法的自适应性。
This method keeps the patches pairs the same sparsity patterns, and makes efficiently use of the self-similar information. The adaptability is enhanced.
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